Improved Methods for Fluorescence Background Subtraction from Raman Spectra

TL;DR

This paper introduces an improved adaptive weighting method for automated fluorescence background subtraction in Raman spectra, enhancing accuracy and robustness across various samples and reducing subjective bias.

Contribution

It presents a novel adaptive weighting scheme applicable to polynomial and penalised least squares methods, improving fluorescence background removal in Raman spectroscopy.

Findings

Method is robust and reliable across simulated spectra.

Significantly improves background fitting accuracy.

Reduces subjectivity in fluorescence subtraction.

Abstract

Raman spectroscopy has attracted interest as a non-invasive optical technique to study the composition and structure of a wide range of materials at the microscopic level. The intrinsic fluorescence background can be orders of magnitude stronger than the Raman scattering and so background removal is one of the foremost challenges for quantitative analysis of Raman spectra in many samples. A range of methods anchored in instrumental and computational programming approaches have been proposed for removing fluorescence background signals. An enhanced adaptive weighting scheme for automated fluorescence removal is reported, applicable to both polynomial fitting and penalised least squares approaches. Analysis of the background fitting results for ensembles of simulated spectra suggests that the method is robust and reliable, and can significantly improve the background fit over the range of signal, shot-noise and background parameters tested, while reducing the subjective nature of the process. The method was also illustrated by application to experimental data generated from aqueous solutions of bulk protein fibrinogen mixed with dextran.